TSLEARN

# Author: Romain Tavenard
# License: BSD 3 clause

import numpy
import matplotlib.pyplot as plt

from tslearn.clustering import TimeSeriesKMeans
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance, \
    TimeSeriesResampler

seed = 0

numpy.random.seed(seed)

X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")

X_train = X_train[y_train < 4]  # Keep first 3 classes

numpy.random.shuffle(X_train)



# Keep only 50 time series
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50])

# Make time series shorter
X_train = TimeSeriesResampler(sz=40).fit_transform(X_train)

sz = X_train.shape[1]



# Euclidean k-means
print("Euclidean k-means")

km = TimeSeriesKMeans(n_clusters=3, verbose=True, random_state=seed)
y_pred = km.fit_predict(X_train)


plt.figure(figsize=(20,20), dpi=200)

# plt.fig(figure_)

for yi in range(3):
    plt.subplot(3, 3, yi + 1)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    plt.text(0.55, 0.85,'Cluster %d' % (yi + 1),
             transform=plt.gca().transAxes)
    if yi == 1:
        plt.title("Euclidean $k$-means")

        
        
# DBA-k-means

print("DBA k-means")

dba_km = TimeSeriesKMeans(n_clusters=3,
                          n_init=2,
                          metric="dtw",
                          verbose=True,
                          max_iter_barycenter=10,
                          random_state=seed)

y_pred = dba_km.fit_predict(X_train)


for yi in range(3):
    plt.subplot(3, 3, 4 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(dba_km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    plt.text(0.55, 0.85,'Cluster %d' % (yi + 1),
             transform=plt.gca().transAxes)
    if yi == 1:
        plt.title("DBA $k$-means")

        
# Soft-DTW-k-means
print("Soft-DTW k-means")
sdtw_km = TimeSeriesKMeans(n_clusters=3,
                           metric="softdtw",
                           metric_params={"gamma": .01},
                           verbose=True,
                           random_state=seed)
y_pred = sdtw_km.fit_predict(X_train)

print(y_pred)
for yi in range(3):
    plt.subplot(3, 3, 7 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(sdtw_km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    plt.text(0.55, 0.85,'Cluster %d' % (yi + 1),
             transform=plt.gca().transAxes)
    if yi == 1:
        plt.title("Soft-DTW $k$-means")

plt.tight_layout()

plt.show()

Euclidean k-means
16.434 --> 9.437 --> 9.437 --> 
DBA k-means
Init 1
0.637 --> 

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

0.458 --> 0.458 --> 
Init 2
0.826 --> 

[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.0s finished

0.525 --> 0.477 --> 0.472 --> 0.472 --> 
Soft-DTW k-means
0.472 --> 0.144 --> 0.142 --> 0.143 --> 0.142 --> 0.143 --> 0.142 --> 0.143 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 0.142 --> 
[0 0 1 0 1 0 2 0 0 1 1 1 0 0 0 1 2 0 2 1 0 1 2 0 2 1 0 1 1 2 0 2 2 0 1 2 0
 2 0 0 0 0 0 0 0 0 1 0 1 2]

# Author: Romain Tavenard
# License: BSD 3 clause

import numpy
import matplotlib.pyplot as plt

from tslearn.clustering import KShape
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance

seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")


# Keep first 3 classes and 50 first time series
X_train = X_train[y_train < 4]
X_train = X_train[:50]
numpy.random.shuffle(X_train)
# For this method to operate properly, prior scaling is required
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train)
sz = X_train.shape[1]

# kShape clustering
ks = KShape(n_clusters=3, verbose=True, random_state=seed)
y_pred = ks.fit_predict(X_train)


plt.figure(figsize=(20,20), dpi=200)
for yi in range(3):
    plt.subplot(3, 1, 1 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
        # dashed small alpha line ! 
    
    plt.plot(ks.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    plt.title("Cluster %d" % (yi + 1))
plt.tight_layout()
plt.show()

0.008 --> 0.006 --> 0.004 --> 0.004 --> 0.004 --> 0.003 --> 0.003 --> 0.003 --> 0.003 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 -->